{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets and DataLoaders\n",
    "\n",
    "We provide Dataset and DataLoader classes for working with Graphein's ``Protein`` data structure. These are analagous to those found in PyTorch and PyTorch Geometric.\n",
    "\n",
    "\n",
    "## 1. List Datasets\n",
    "\n",
    "List datasets are the most straightforward for datasets that are small enough to fit in memory. They are also very flexible - all the preprocessing is offloaded to the user - and simply wrap a list of Proteins (or Data) into a dataset.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/a-r-j/graphein/blob/master/notebooks/protein_tensors.ipynb) [![GitHub](https://img.shields.io/badge/-View%20on%20GitHub-181717?logo=github&logoColor=ffffff)](https://github.com/a-r-j/graphein/blob/master/notebooks/protein_tensors.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphein\n",
    "graphein.verbose(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:04<00:00,  4.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='4hhb', y=[1]), Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='3eiy', y=[1])]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# First, create a list of Protein objects\n",
    "import torch\n",
    "from graphein.protein.tensor.data import to_protein_mp\n",
    "\n",
    "# Create a List of 20 Protein objects using multiprocessing\n",
    "data_list = to_protein_mp(pdb_codes=[\"4hhb\", \"3eiy\"] * 10, num_cores=4)\n",
    "\n",
    "# Optional: do some processing on the data\n",
    "def processing_pipeline(protein):\n",
    "    # Do some processing\n",
    "    # E.g. assign a random label:\n",
    "    protein.y = torch.rand(1)\n",
    "    return protein\n",
    "\n",
    "data_list = [processing_pipeline(protein) for protein in data_list]\n",
    "\n",
    "print(data_list)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now wrap this into a ``ProteinListDataset``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:  ProteinGraphListDataset(20)\n",
      "Example:  Protein(fill_value=[1], atom_list=[37], chains=[574], residue_type=[574], x=[574, 37, 3], id='4hhb', residue_id=[574], residues=[574], y=[1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['pre_filter.pt',\n",
       " 'data_test.pt',\n",
       " 'data_list_dataset_tests.pt',\n",
       " 'pre_transform.pt',\n",
       " 'list_dataset_tests.pt']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphein.protein.tensor.dataset import ProteinGraphListDataset\n",
    "\n",
    "list_dataset = ProteinGraphListDataset(\n",
    "    root=\".\",\n",
    "    name=\"list_dataset_tests\",\n",
    "    data_list=data_list\n",
    "    )\n",
    "print(\"Dataset: \", list_dataset)\n",
    "print(\"Example: \", list_dataset[4])\n",
    "\n",
    "import os\n",
    "os.listdir(\"./processed/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Protein(fill_value=[20], atom_list=[20], chains=[7480], residue_type=[7480], x=[7480, 37, 3], id=[20], residue_id=[20], residues=[20], y=[20]),\n",
       " defaultdict(dict,\n",
       "             {'fill_value': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                      18, 19, 20]),\n",
       "              'atom_list': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                      18, 19, 20]),\n",
       "              'chains': tensor([   0,  574,  748, 1322, 1496, 2070, 2244, 2818, 2992, 3566, 3740, 4314,\n",
       "                      4488, 5062, 5236, 5810, 5984, 6558, 6732, 7306, 7480]),\n",
       "              'residue_type': tensor([   0,  574,  748, 1322, 1496, 2070, 2244, 2818, 2992, 3566, 3740, 4314,\n",
       "                      4488, 5062, 5236, 5810, 5984, 6558, 6732, 7306, 7480]),\n",
       "              'x': tensor([   0,  574,  748, 1322, 1496, 2070, 2244, 2818, 2992, 3566, 3740, 4314,\n",
       "                      4488, 5062, 5236, 5810, 5984, 6558, 6732, 7306, 7480]),\n",
       "              'id': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                      18, 19, 20]),\n",
       "              'residue_id': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                      18, 19, 20]),\n",
       "              'residues': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                      18, 19, 20]),\n",
       "              'y': tensor([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                      18, 19, 20])}))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.load(\"./processed/list_dataset_tests.pt\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Datasets\n",
    "\n",
    "### From PDB Codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 56936.25it/s]\n",
      "Processing...\n",
      "100%|██████████| 3/3 [00:00<00:00, 25.15it/s]\n",
      "1it [00:00,  3.71it/s]\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from graphein.protein.tensor.dataset import ProteinDataset\n",
    "\n",
    "# From PDB Codes:\n",
    "\n",
    "ds = ProteinDataset(\n",
    "    pdb_codes=[\"4hhb\", \"3eiy\", \"5caj\"],\n",
    "    root=\".\",\n",
    "    pdb_dir=\"./pdbs/\",\n",
    "    out_dir=\"./processed_files/\",\n",
    "    overwrite=True,\n",
    "    graph_labels=[torch.rand(4), torch.rand(4), torch.rand(4)],\n",
    "    node_labels=[torch.rand(10), torch.rand(10), torch.rand(10)],\n",
    "    chain_selections=[\"all\", \"all\", \"all\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PDBs:  ['0.pdb', '4hhb.pdb', '1.pdb', '2.pdb', '3eiy.pdb', '5caj.pdb']\n",
      "tensors:  ['pre_filter.pt', '0_all.pt', '1_all.pt', 'pre_transform.pt', '5caj_all.pt', '2_all.pt', '3eiy_all.pt', '4hhb_all.pt']\n",
      "Protein(fill_value=1e-05, atom_list=[37], chains=[574], residue_type=[574], residues=[574], x=[574, 37, 3], residue_id=[574], id='./pdbs//4hhb.pdb', graph_y=[4], node_y=[10]) Protein(fill_value=1e-05, atom_list=[37], chains=[174], residue_type=[174], residues=[174], x=[174, 37, 3], residue_id=[174], id='./pdbs//3eiy.pdb', graph_y=[4], node_y=[10]) Protein(fill_value=1e-05, atom_list=[37], chains=[510], residue_type=[510], residues=[510], x=[510, 37, 3], residue_id=[510], id='./pdbs//5caj.pdb', graph_y=[4], node_y=[10])\n"
     ]
    }
   ],
   "source": [
    "print(\"PDBs: \", os.listdir(\"pdbs\"))\n",
    "print(\"tensors: \", os.listdir(\"./processed_files/\"))\n",
    "print(ds[0], ds[1], ds[2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From Sequences (via ESMFold)\n",
    "\n",
    "(Sturctural) Datasets can be generated using ESMFold from amino acid sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:29<00:00,  9.75s/it]\n",
      "100%|██████████| 3/3 [00:00<00:00, 21546.08it/s]\n",
      "Processing...\n",
      "100%|██████████| 3/3 [00:00<00:00, 104.12it/s]\n",
      "1it [00:01,  1.07s/it]\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "ds = ProteinDataset(\n",
    "    sequences=[\"AGYFGMTAME\", \"AGYFGMTAME\", \"AGYFGMTAME\"], # Also accepts a path to a FASTA file.\n",
    "    root=\".\",\n",
    "    pdb_dir=\"./pdbs/\",\n",
    "    out_dir=\"./processed_files/\",\n",
    "    overwrite=True,\n",
    "    graph_labels=[torch.rand(1), torch.rand(1), torch.rand(1)],\n",
    "    node_labels=[torch.rand(16), torch.rand(14), torch.rand(12)],\n",
    "    chain_selections=[\"all\", \"all\", \"all\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PDBs:  ['0.pdb', '4hhb.pdb', '1.pdb', '2.pdb', '3eiy.pdb', '5caj.pdb']\n",
      "tensors:  ['pre_filter.pt', '0_all.pt', '1_all.pt', 'pre_transform.pt', '5caj_all.pt', '2_all.pt', '3eiy_all.pt', '4hhb_all.pt']\n",
      "Protein(fill_value=1e-05, atom_list=[37], chains=[10], residue_type=[10], residues=[10], x=[10, 37, 3], residue_id=[10], id='./pdbs//0.pdb', graph_y=[1], node_y=[16]) Protein(fill_value=1e-05, atom_list=[37], chains=[10], residue_type=[10], residues=[10], x=[10, 37, 3], residue_id=[10], id='./pdbs//1.pdb', graph_y=[1], node_y=[14]) Protein(fill_value=1e-05, atom_list=[37], chains=[10], residue_type=[10], residues=[10], x=[10, 37, 3], residue_id=[10], id='./pdbs//2.pdb', graph_y=[1], node_y=[12])\n"
     ]
    }
   ],
   "source": [
    "print(\"PDBs: \", os.listdir(\"pdbs\"))\n",
    "print(\"tensors: \", os.listdir(\"./processed_files/\"))\n",
    "print(ds[0], ds[1], ds[2])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From PDB Files\n",
    "\n",
    "Datasets can be created from collections of local structure files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[01/27/23 02:30:16] </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> To use the Graphein submodule                <a href=\"file:///home/atj39/github/graphein/graphein/protein/visualisation.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">visualisation.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/atj39/github/graphein/graphein/protein/visualisation.py#35\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">35</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         graphein.protein.visualisation, you need to  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         install: pytorch3d                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         To do so, use the following command: conda   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                   </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         install -c pytorch3d pytorch3d               <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                   </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[01/27/23 02:30:16]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m To use the Graphein submodule                \u001b]8;id=999859;file:///home/atj39/github/graphein/graphein/protein/visualisation.py\u001b\\\u001b[2mvisualisation.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=289184;file:///home/atj39/github/graphein/graphein/protein/visualisation.py#35\u001b\\\u001b[2m35\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                    \u001b[0m         graphein.protein.visualisation, you need to  \u001b[2m                   \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         install: pytorch3d                           \u001b[2m                   \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         To do so, use the following command: conda   \u001b[2m                   \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         install -c pytorch3d pytorch3d               \u001b[2m                   \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING </span> To use the Graphein submodule                       <a href=\"file:///home/atj39/github/graphein/graphein/protein/meshes.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">meshes.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/atj39/github/graphein/graphein/protein/meshes.py#29\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">29</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         graphein.protein.meshes, you need to install:       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         pytorch3d                                           <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         To do so, use the following command: conda install  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         -c pytorch3d pytorch3d                              <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">            </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m                   \u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m To use the Graphein submodule                       \u001b]8;id=529324;file:///home/atj39/github/graphein/graphein/protein/meshes.py\u001b\\\u001b[2mmeshes.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=54075;file:///home/atj39/github/graphein/graphein/protein/meshes.py#29\u001b\\\u001b[2m29\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m                    \u001b[0m         graphein.protein.meshes, you need to install:       \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         pytorch3d                                           \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         To do so, use the following command: conda install  \u001b[2m            \u001b[0m\n",
       "\u001b[2;36m                    \u001b[0m         -c pytorch3d pytorch3d                              \u001b[2m            \u001b[0m\n"
      ]
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     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Creating a dataset from <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span> structures.             <a href=\"file:///home/atj39/github/graphein/graphein/protein/tensor/dataset.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">dataset.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///home/atj39/github/graphein/graphein/protein/tensor/dataset.py#192\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">192</span></a>\n",
       "</pre>\n"
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">                    </span>         overwritten.                                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">              </span>\n",
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      "100%|██████████| 3/3 [00:00<00:00, 14446.51it/s]\n",
      "Processing...\n"
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     "name": "stderr",
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     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./pdbs/4hhb.pdb', './pdbs/3eiy.pdb', './pdbs/5caj.pdb']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 11.73it/s]\n",
      "1it [00:00,  2.45it/s]\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from graphein.protein.tensor.dataset import ProteinDataset\n",
    "\n",
    "# From PDB Codes:\n",
    "\n",
    "ds = ProteinDataset(\n",
    "    pdb_paths=[\"4hhb.pdb\", \"3eiy.pdb\", \"5caj.pdb\"],\n",
    "    root=\".\",\n",
    "    pdb_dir=\"./pdbs\",\n",
    "    out_dir=\"./processed_files/\",\n",
    "    overwrite=True,\n",
    "    graph_labels=[torch.rand(4), torch.rand(4), torch.rand(4)],\n",
    "    node_labels=[torch.rand(10), torch.rand(10), torch.rand(10)],\n",
    "    chain_selections=[\"all\", \"all\", \"all\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PDBs:  ['0.pdb', '4hhb.pdb', '1.pdb', '2.pdb', '3eiy.pdb', '5caj.pdb']\n",
      "tensors:  ['pre_filter.pt', '0_all.pt', '1_all.pt', 'pre_transform.pt', '5caj_all.pt', '2_all.pt', '3eiy_all.pt', '4hhb_all.pt']\n",
      "Protein(fill_value=1e-05, atom_list=[37], x=[574, 37, 3], residue_type=[574], residues=[574], chains=[574], residue_id=[574], id='./pdbs/4hhb.pdb', graph_y=[4], node_y=[10]) Protein(fill_value=1e-05, atom_list=[37], x=[174, 37, 3], residue_type=[174], residues=[174], chains=[174], residue_id=[174], id='./pdbs/3eiy.pdb', graph_y=[4], node_y=[10]) Protein(fill_value=1e-05, atom_list=[37], x=[510, 37, 3], residue_type=[510], residues=[510], chains=[510], residue_id=[510], id='./pdbs/5caj.pdb', graph_y=[4], node_y=[10])\n"
     ]
    }
   ],
   "source": [
    "print(\"PDBs: \", os.listdir(\"pdbs\"))\n",
    "print(\"tensors: \", os.listdir(\"./processed_files/\"))\n",
    "print(ds[0], ds[1], ds[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size:  3\n",
      "Num batches:  1\n",
      "Example batch:  ProteinProteinBatch(fill_value=[3], atom_list=[3], x=[1258, 37, 3], residue_type=[1258], residues=[3], chains=[1258], residue_id=[3], id=[3], graph_y=[12], node_y=[30], batch=[1258], ptr=[4])\n",
      "Num graphs:  3\n",
      "Num nodes:  1258\n"
     ]
    }
   ],
   "source": [
    "from graphein.protein.tensor.dataloader import ProteinDataLoader\n",
    "\n",
    "dl = ProteinDataLoader(ds, batch_size=4, shuffle=True, num_workers=0, pin_memory=False)\n",
    "\n",
    "print(\"Dataset size: \", len(ds))\n",
    "print(\"Num batches: \", len(dl))\n",
    "\n",
    "for batch in dl:\n",
    "    print(\"Example batch: \", batch)\n",
    "    print(\"Num graphs: \", batch.num_graphs)\n",
    "    print(\"Num nodes: \", batch.num_nodes)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Protein(fill_value=[1], atom_list=[37], x=[174, 37, 3], residue_type=[174], residues=[174], chains=[174], residue_id=[174], id='./pdbs/3eiy.pdb', graph_y=[4], node_y=[10]),\n",
       " Protein(fill_value=[1], atom_list=[37], x=[510, 37, 3], residue_type=[510], residues=[510], chains=[510], residue_id=[510], id='./pdbs/5caj.pdb', graph_y=[4], node_y=[10]),\n",
       " Protein(fill_value=[1], atom_list=[37], x=[574, 37, 3], residue_type=[574], residues=[574], chains=[574], residue_id=[574], id='./pdbs/4hhb.pdb', graph_y=[4], node_y=[10])]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.to_protein_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
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